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| # | |
| input: "data" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 1024 | |
| input_dim: 2048 | |
| layer { | |
| name: "data_sub1" | |
| type: "Scale" | |
| bottom: "data" | |
| top: "data_sub1" | |
| } | |
| layer { | |
| name: "data_sub2" | |
| type: "Interp" | |
| bottom: "data_sub1" | |
| top: "data_sub2" | |
| interp_param { | |
| shrink_factor: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv1_1_3x3_s2" | |
| type: "Convolution" | |
| bottom: "data_sub2" | |
| top: "conv1_1_3x3_s2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_1_3x3_s2/relu" | |
| type: "ReLU" | |
| bottom: "conv1_1_3x3_s2" | |
| top: "conv1_1_3x3_s2" | |
| } | |
| layer { | |
| name: "conv1_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv1_1_3x3_s2" | |
| top: "conv1_2_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv1_2_3x3" | |
| top: "conv1_2_3x3" | |
| } | |
| layer { | |
| name: "conv1_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv1_2_3x3" | |
| top: "conv1_3_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv1_3_3x3" | |
| top: "conv1_3_3x3" | |
| } | |
| layer { | |
| name: "pool1_3x3_s2" | |
| type: "Pooling" | |
| bottom: "conv1_3_3x3" | |
| top: "pool1_3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "pool1_3x3_s2" | |
| top: "conv2_1_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1_1x1_reduce" | |
| top: "conv2_1_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv2_1_3x3" | |
| type: "Convolution" | |
| bottom: "conv2_1_1x1_reduce" | |
| top: "conv2_1_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1_3x3" | |
| top: "conv2_1_3x3" | |
| } | |
| layer { | |
| name: "conv2_1_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv2_1_3x3" | |
| top: "conv2_1_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1_1x1_proj" | |
| type: "Convolution" | |
| bottom: "pool1_3x3_s2" | |
| top: "conv2_1_1x1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Eltwise" | |
| bottom: "conv2_1_1x1_proj" | |
| bottom: "conv2_1_1x1_increase" | |
| top: "conv2_1" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/relu" | |
| type: "ReLU" | |
| bottom: "conv2_1" | |
| top: "conv2_1" | |
| } | |
| layer { | |
| name: "conv2_2_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv2_1" | |
| top: "conv2_2_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2_1x1_reduce" | |
| top: "conv2_2_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv2_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv2_2_1x1_reduce" | |
| top: "conv2_2_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2_3x3" | |
| top: "conv2_2_3x3" | |
| } | |
| layer { | |
| name: "conv2_2_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv2_2_3x3" | |
| top: "conv2_2_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2" | |
| type: "Eltwise" | |
| bottom: "conv2_1" | |
| bottom: "conv2_2_1x1_increase" | |
| top: "conv2_2" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/relu" | |
| type: "ReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| name: "conv2_3_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv2_2" | |
| top: "conv2_3_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3_1x1_reduce" | |
| top: "conv2_3_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv2_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv2_3_1x1_reduce" | |
| top: "conv2_3_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3_3x3" | |
| top: "conv2_3_3x3" | |
| } | |
| layer { | |
| name: "conv2_3_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv2_3_3x3" | |
| top: "conv2_3_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_3" | |
| type: "Eltwise" | |
| bottom: "conv2_2" | |
| bottom: "conv2_3_1x1_increase" | |
| top: "conv2_3" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv2_3/relu" | |
| type: "ReLU" | |
| bottom: "conv2_3" | |
| top: "conv2_3" | |
| } | |
| layer { | |
| name: "conv3_1_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv2_3" | |
| top: "conv3_1_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1_1x1_reduce" | |
| top: "conv3_1_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv3_1_3x3" | |
| type: "Convolution" | |
| bottom: "conv3_1_1x1_reduce" | |
| top: "conv3_1_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1_3x3" | |
| top: "conv3_1_3x3" | |
| } | |
| layer { | |
| name: "conv3_1_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv3_1_3x3" | |
| top: "conv3_1_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_1x1_proj" | |
| type: "Convolution" | |
| bottom: "conv2_3" | |
| top: "conv3_1_1x1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Eltwise" | |
| bottom: "conv3_1_1x1_proj" | |
| bottom: "conv3_1_1x1_increase" | |
| top: "conv3_1" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/relu" | |
| type: "ReLU" | |
| bottom: "conv3_1" | |
| top: "conv3_1" | |
| } | |
| layer { | |
| name: "conv3_1_sub4" | |
| type: "Interp" | |
| bottom: "conv3_1" | |
| top: "conv3_1_sub4" | |
| interp_param { | |
| shrink_factor: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv3_1_sub4" | |
| top: "conv3_2_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2_1x1_reduce" | |
| top: "conv3_2_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv3_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv3_2_1x1_reduce" | |
| top: "conv3_2_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2_3x3" | |
| top: "conv3_2_3x3" | |
| } | |
| layer { | |
| name: "conv3_2_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv3_2_3x3" | |
| top: "conv3_2_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2" | |
| type: "Eltwise" | |
| bottom: "conv3_1_sub4" | |
| bottom: "conv3_2_1x1_increase" | |
| top: "conv3_2" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/relu" | |
| type: "ReLU" | |
| bottom: "conv3_2" | |
| top: "conv3_2" | |
| } | |
| layer { | |
| name: "conv3_3_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv3_2" | |
| top: "conv3_3_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3_1x1_reduce" | |
| top: "conv3_3_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv3_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv3_3_1x1_reduce" | |
| top: "conv3_3_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3_3x3" | |
| top: "conv3_3_3x3" | |
| } | |
| layer { | |
| name: "conv3_3_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv3_3_3x3" | |
| top: "conv3_3_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_3" | |
| type: "Eltwise" | |
| bottom: "conv3_2" | |
| bottom: "conv3_3_1x1_increase" | |
| top: "conv3_3" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv3_3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "conv3_4_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv3_3" | |
| top: "conv3_4_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4_1x1_reduce" | |
| top: "conv3_4_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv3_4_3x3" | |
| type: "Convolution" | |
| bottom: "conv3_4_1x1_reduce" | |
| top: "conv3_4_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_4_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4_3x3" | |
| top: "conv3_4_3x3" | |
| } | |
| layer { | |
| name: "conv3_4_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv3_4_3x3" | |
| top: "conv3_4_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_4" | |
| type: "Eltwise" | |
| bottom: "conv3_3" | |
| bottom: "conv3_4_1x1_increase" | |
| top: "conv3_4" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv3_4/relu" | |
| type: "ReLU" | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| } | |
| layer { | |
| name: "conv4_1_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv3_4" | |
| top: "conv4_1_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1_1x1_reduce" | |
| top: "conv4_1_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_1_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_1_1x1_reduce" | |
| top: "conv4_1_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1_3x3" | |
| top: "conv4_1_3x3" | |
| } | |
| layer { | |
| name: "conv4_1_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_1_3x3" | |
| top: "conv4_1_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1_1x1_proj" | |
| type: "Convolution" | |
| bottom: "conv3_4" | |
| top: "conv4_1_1x1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Eltwise" | |
| bottom: "conv4_1_1x1_proj" | |
| bottom: "conv4_1_1x1_increase" | |
| top: "conv4_1" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/relu" | |
| type: "ReLU" | |
| bottom: "conv4_1" | |
| top: "conv4_1" | |
| } | |
| layer { | |
| name: "conv4_2_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_1" | |
| top: "conv4_2_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2_1x1_reduce" | |
| top: "conv4_2_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_2_1x1_reduce" | |
| top: "conv4_2_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2_3x3" | |
| top: "conv4_2_3x3" | |
| } | |
| layer { | |
| name: "conv4_2_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_2_3x3" | |
| top: "conv4_2_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2" | |
| type: "Eltwise" | |
| bottom: "conv4_1" | |
| bottom: "conv4_2_1x1_increase" | |
| top: "conv4_2" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/relu" | |
| type: "ReLU" | |
| bottom: "conv4_2" | |
| top: "conv4_2" | |
| } | |
| layer { | |
| name: "conv4_3_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_2" | |
| top: "conv4_3_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3_1x1_reduce" | |
| top: "conv4_3_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_3_1x1_reduce" | |
| top: "conv4_3_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3_3x3" | |
| top: "conv4_3_3x3" | |
| } | |
| layer { | |
| name: "conv4_3_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_3_3x3" | |
| top: "conv4_3_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_3" | |
| type: "Eltwise" | |
| bottom: "conv4_2" | |
| bottom: "conv4_3_1x1_increase" | |
| top: "conv4_3" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv4_3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| name: "conv4_4_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_3" | |
| top: "conv4_4_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4_1x1_reduce" | |
| top: "conv4_4_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_4_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_4_1x1_reduce" | |
| top: "conv4_4_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_4_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4_3x3" | |
| top: "conv4_4_3x3" | |
| } | |
| layer { | |
| name: "conv4_4_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_4_3x3" | |
| top: "conv4_4_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_4" | |
| type: "Eltwise" | |
| bottom: "conv4_3" | |
| bottom: "conv4_4_1x1_increase" | |
| top: "conv4_4" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv4_4/relu" | |
| type: "ReLU" | |
| bottom: "conv4_4" | |
| top: "conv4_4" | |
| } | |
| layer { | |
| name: "conv4_5_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_4" | |
| top: "conv4_5_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_5_1x1_reduce" | |
| top: "conv4_5_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_5_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_5_1x1_reduce" | |
| top: "conv4_5_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_5_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_5_3x3" | |
| top: "conv4_5_3x3" | |
| } | |
| layer { | |
| name: "conv4_5_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_5_3x3" | |
| top: "conv4_5_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_5" | |
| type: "Eltwise" | |
| bottom: "conv4_4" | |
| bottom: "conv4_5_1x1_increase" | |
| top: "conv4_5" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv4_5/relu" | |
| type: "ReLU" | |
| bottom: "conv4_5" | |
| top: "conv4_5" | |
| } | |
| layer { | |
| name: "conv4_6_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_5" | |
| top: "conv4_6_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv4_6_1x1_reduce" | |
| top: "conv4_6_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv4_6_3x3" | |
| type: "Convolution" | |
| bottom: "conv4_6_1x1_reduce" | |
| top: "conv4_6_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4_6_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv4_6_3x3" | |
| top: "conv4_6_3x3" | |
| } | |
| layer { | |
| name: "conv4_6_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv4_6_3x3" | |
| top: "conv4_6_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_6" | |
| type: "Eltwise" | |
| bottom: "conv4_5" | |
| bottom: "conv4_6_1x1_increase" | |
| top: "conv4_6" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv4_6/relu" | |
| type: "ReLU" | |
| bottom: "conv4_6" | |
| top: "conv4_6" | |
| } | |
| layer { | |
| name: "conv5_1_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv4_6" | |
| top: "conv5_1_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1_1x1_reduce" | |
| top: "conv5_1_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv5_1_3x3" | |
| type: "Convolution" | |
| bottom: "conv5_1_1x1_reduce" | |
| top: "conv5_1_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 4 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1_3x3" | |
| top: "conv5_1_3x3" | |
| } | |
| layer { | |
| name: "conv5_1_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv5_1_3x3" | |
| top: "conv5_1_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1_1x1_proj" | |
| type: "Convolution" | |
| bottom: "conv4_6" | |
| top: "conv5_1_1x1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1" | |
| type: "Eltwise" | |
| bottom: "conv5_1_1x1_proj" | |
| bottom: "conv5_1_1x1_increase" | |
| top: "conv5_1" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/relu" | |
| type: "ReLU" | |
| bottom: "conv5_1" | |
| top: "conv5_1" | |
| } | |
| layer { | |
| name: "conv5_2_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv5_1" | |
| top: "conv5_2_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2_1x1_reduce" | |
| top: "conv5_2_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv5_2_3x3" | |
| type: "Convolution" | |
| bottom: "conv5_2_1x1_reduce" | |
| top: "conv5_2_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 4 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2_3x3" | |
| top: "conv5_2_3x3" | |
| } | |
| layer { | |
| name: "conv5_2_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv5_2_3x3" | |
| top: "conv5_2_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2" | |
| type: "Eltwise" | |
| bottom: "conv5_1" | |
| bottom: "conv5_2_1x1_increase" | |
| top: "conv5_2" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/relu" | |
| type: "ReLU" | |
| bottom: "conv5_2" | |
| top: "conv5_2" | |
| } | |
| layer { | |
| name: "conv5_3_1x1_reduce" | |
| type: "Convolution" | |
| bottom: "conv5_2" | |
| top: "conv5_3_1x1_reduce" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_1x1_reduce/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3_1x1_reduce" | |
| top: "conv5_3_1x1_reduce" | |
| } | |
| layer { | |
| name: "conv5_3_3x3" | |
| type: "Convolution" | |
| bottom: "conv5_3_1x1_reduce" | |
| top: "conv5_3_3x3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 4 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 4 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_3x3/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3_3x3" | |
| top: "conv5_3_3x3" | |
| } | |
| layer { | |
| name: "conv5_3_1x1_increase" | |
| type: "Convolution" | |
| bottom: "conv5_3_3x3" | |
| top: "conv5_3_1x1_increase" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_3" | |
| type: "Eltwise" | |
| bottom: "conv5_2" | |
| bottom: "conv5_3_1x1_increase" | |
| top: "conv5_3" | |
| eltwise_param { | |
| operation: SUM | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/relu" | |
| type: "ReLU" | |
| bottom: "conv5_3" | |
| top: "conv5_3" | |
| } | |
| layer { | |
| name: "conv5_3_pool1" | |
| type: "Pooling" | |
| bottom: "conv5_3" | |
| top: "conv5_3_pool1" | |
| pooling_param { | |
| pool: AVE | |
| kernel_h: 33 | |
| kernel_w: 65 | |
| stride_h: 33 | |
| stride_w: 65 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_pool1_interp" | |
| type: "Interp" | |
| bottom: "conv5_3_pool1" | |
| top: "conv5_3_pool1_interp" | |
| interp_param { | |
| height: 33 | |
| width: 65 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_pool2" | |
| type: "Pooling" | |
| bottom: "conv5_3" | |
| top: "conv5_3_pool2" | |
| pooling_param { | |
| pool: AVE | |
| kernel_h: 17 | |
| kernel_w: 33 | |
| stride_h: 16 | |
| stride_w: 32 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_pool2_interp" | |
| type: "Interp" | |
| bottom: "conv5_3_pool2" | |
| top: "conv5_3_pool2_interp" | |
| interp_param { | |
| height: 33 | |
| width: 65 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_pool3" | |
| type: "Pooling" | |
| bottom: "conv5_3" | |
| top: "conv5_3_pool3" | |
| pooling_param { | |
| pool: AVE | |
| kernel_h: 13 | |
| kernel_w: 25 | |
| stride_h: 10 | |
| stride_w: 20 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_pool3_interp" | |
| type: "Interp" | |
| bottom: "conv5_3_pool3" | |
| top: "conv5_3_pool3_interp" | |
| interp_param { | |
| height: 33 | |
| width: 65 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_pool6" | |
| type: "Pooling" | |
| bottom: "conv5_3" | |
| top: "conv5_3_pool6" | |
| pooling_param { | |
| pool: AVE | |
| kernel_h: 8 | |
| kernel_w: 15 | |
| stride_h: 5 | |
| stride_w: 10 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_pool6_interp" | |
| type: "Interp" | |
| bottom: "conv5_3_pool6" | |
| top: "conv5_3_pool6_interp" | |
| interp_param { | |
| height: 33 | |
| width: 65 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3_sum" | |
| type: "Eltwise" | |
| bottom: "conv5_3" | |
| bottom: "conv5_3_pool6_interp" | |
| bottom: "conv5_3_pool3_interp" | |
| bottom: "conv5_3_pool2_interp" | |
| bottom: "conv5_3_pool1_interp" | |
| top: "conv5_3_sum" | |
| } | |
| layer { | |
| name: "conv5_4_k1" | |
| type: "Convolution" | |
| bottom: "conv5_3_sum" | |
| top: "conv5_4_k1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_4_k1/relu" | |
| type: "ReLU" | |
| bottom: "conv5_4_k1" | |
| top: "conv5_4_k1" | |
| } | |
| layer { | |
| name: "conv5_4_interp" | |
| type: "Interp" | |
| bottom: "conv5_4_k1" | |
| top: "conv5_4_interp" | |
| interp_param { | |
| zoom_factor: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv_sub4" | |
| type: "Convolution" | |
| bottom: "conv5_4_interp" | |
| top: "conv_sub4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1_sub2_proj" | |
| type: "Convolution" | |
| bottom: "conv3_1" | |
| top: "conv3_1_sub2_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub24_sum" | |
| type: "Eltwise" | |
| bottom: "conv3_1_sub2_proj" | |
| bottom: "conv_sub4" | |
| top: "sub24_sum" | |
| } | |
| layer { | |
| name: "sub24_sum/relu" | |
| type: "ReLU" | |
| bottom: "sub24_sum" | |
| top: "sub24_sum" | |
| } | |
| layer { | |
| name: "sub24_sum_interp" | |
| type: "Interp" | |
| bottom: "sub24_sum" | |
| top: "sub24_sum_interp" | |
| interp_param { | |
| zoom_factor: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv_sub2" | |
| type: "Convolution" | |
| bottom: "sub24_sum_interp" | |
| top: "conv_sub2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 3 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| dilation: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv1_sub1" | |
| type: "Convolution" | |
| bottom: "data_sub1" | |
| top: "conv1_sub1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1_sub1/relu" | |
| type: "ReLU" | |
| bottom: "conv1_sub1" | |
| top: "conv1_sub1" | |
| } | |
| layer { | |
| name: "conv2_sub1" | |
| type: "Convolution" | |
| bottom: "conv1_sub1" | |
| top: "conv2_sub1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_sub1/relu" | |
| type: "ReLU" | |
| bottom: "conv2_sub1" | |
| top: "conv2_sub1" | |
| } | |
| layer { | |
| name: "conv3_sub1" | |
| type: "Convolution" | |
| bottom: "conv2_sub1" | |
| top: "conv3_sub1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_sub1/relu" | |
| type: "ReLU" | |
| bottom: "conv3_sub1" | |
| top: "conv3_sub1" | |
| } | |
| layer { | |
| name: "conv3_sub1_proj" | |
| type: "Convolution" | |
| bottom: "conv3_sub1" | |
| top: "conv3_sub1_proj" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "sub12_sum" | |
| type: "Eltwise" | |
| bottom: "conv3_sub1_proj" | |
| bottom: "conv_sub2" | |
| top: "sub12_sum" | |
| } | |
| layer { | |
| name: "sub12_sum/relu" | |
| type: "ReLU" | |
| bottom: "sub12_sum" | |
| top: "sub12_sum" | |
| } | |
| layer { | |
| name: "sub12_sum_interp" | |
| type: "Interp" | |
| bottom: "sub12_sum" | |
| top: "sub12_sum_interp" | |
| interp_param { | |
| zoom_factor: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv6_cls" | |
| type: "Convolution" | |
| bottom: "sub12_sum_interp" | |
| top: "conv6_cls" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 19 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6_interp" | |
| type: "Interp" | |
| bottom: "conv6_cls" | |
| top: "conv6_interp" | |
| interp_param { | |
| zoom_factor: 4 | |
| } | |
| } |
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